Method and system for enhancing the yield in semiconductor manufacturing

ABSTRACT

Roughly described, a manufacturing process is enhanced by using TCAD and TCAD-derived models. A TCAD simulation model of the process is developed, which predicts, in dependence upon a plurality of process input parameters, a value for a performance parameter of a product to be manufactured using the process. Estimated, predicted or desired values for a calculated subset of the parameters (including either process input parameters or product performance parameters or both), are determined in dependence upon the process model, and further in dependence upon actual, estimated or desired values for a different subset of the parameters (again either process input parameters or product performance parameters or both). The determination is preferably made using a process compact model of the process, itself developed in dependence upon the simulation model.

CROSS-REFERENCES

This application is a Continuation of U.S. patent application Ser. No.11/139,110 filed 27 May 2005, which is a Continuation-In-Part of U.S.patent application Ser. No. 11/092,967 filed 29 Mar. 2005, which claimsthe benefit of Provisional Application No. 60/557,382 filed 30 Mar.2004. All parent applications are incorporated herein by reference intheir entirety.

1. FIELD OF THE INVENTION

The invention relates to a method for enhancing the yield insemiconductor manufacturing, a computer program product and a system forenhancing the yield in semiconductor manufacturing.

2. BACKGROUND AND SUMMARY OF THE INVENTION

Technology Computer Aided Design (TCAD) uses physics-based computersimulations to design, analyze, and optimize semiconductor devices. TCADrepresents the available physical knowledge of semiconductor processingand devices in terms of computer models. It represents devices as onedimensional, two-dimensional or threedimensional finite-element orfinite-volume models. Each element represents a piece of a certainmaterial, with certain properties. TCAD numerically solves partialdifferential equations in space and/or time with appropriate boundaryconditions. Typically this is done with finite element or finite volumeanalysis, although in some cases other methods for solving the partialdifferential equations can be used, such as particle/atomistic methods.

TCAD consists of two major components:

-   1. Process simulation is modeling semiconductor manufacturing    processes. The simulation starts with the bare wafer and finishes    with device structures. Processes such as implantation, diffusion,    etching, growth, and deposition processes are simulated on a    microscopic level.-   2. Device simulation is modeling the semiconductor device operation    on a microscopic level. By integrating microscopic currents, the    electrical behavior is characterized. SPICE model parameters can be    extracted from the simulated electrical characteristics.

Today's TCAD tools are capable of modeling the entire semiconductormanufacturing process and product performance with physical models ofvarying sophistication. Typically, from the simulation-derived devicecharacteristics it is possible to extract model parameters for so-called(lumped) circuit models, i.e., which can be used in circuit simulatorssuch as SPICE, which is often used as a central tool in ElectronicComputer Aided Design (ECAD) to generate circuits.

While in TCAD typically few semiconductor devices are simulated withvery high sophistication in terms of physical models, in ECAD moredevices can be simulated, but the models for the individual devices areenormously reduced in complexity and sophistication. TCAD is applicableto all semiconductor devices, notably diodes, transistors, opticaldevices such as LEDs, lasers, specific test structures for processcontrol, and others.

It is known to use circuit simulations during the manufacturing processof a semiconductor product. E.g. in JP 2001/188816 a method formanufacturing a transistor is described, where the gate length and thegate width are calculated using a circuit model. The calculation isperformed on the basis of measurement results during the manufacturing.

TCAD allows an understanding of the manufacturing process and theoperation of semiconductor devices and is therefore often used inresearch and development for the development of new processes anddevices. Notably, it allows to save cost by reducing the number ofcostly experiments.

One goal of the present invention is to use TCAD to address issues ofprocess and device variability in manufacturing. The simulationexperiments in TCAD have the advantage that every process condition canbe accurately controlled, and that arbitrary product performancecharacteristics can be determined. This is as opposed to realexperiments, where the control of process steps may be difficult andsubject to uncontrollable drift or variation in the equipment, and wherethe limitations of metrology can make it difficult, expensive orimpossible to make measurements both in non-destructive and destructivemeasurements.

In particular, it is important to improve the systematic yield insemiconductor manufacturing of products where the structure of thesemiconductor product is smaller than 130 nm. In this range the yield isincreasingly subject to other limiting factors than just defects.Notably, addressing the parametric yield issues that arise throughprocess and device variability is very important. Nonetheless, aspectsof the present invention can be used advantageously also for structuresabove 130 nm.

Unfortunately, while TCAD simulations can be made very accurate, theyare still very time consuming to execute. A single reasonably accurate2-dimensional simulation of a MOSFET device may take on the order of anhour or more to execute. This deficiency severely limits thepracticality of using TCAD in manufacturing, as opposed to design anddevelopment.

TCAD allows users to model the semiconductor manufacturing also in formof process models, where the semiconductor manufacturing process isdescribed in terms of a number of input parameters and outputparameters. Such a model is referred to as a TCAD Process Model. Inaccordance with an aspect of the invention, roughly described,simplified models or TCAD Process Compact Models (PCMs) can be derivedfrom the TCAD Process Model, to describe the connection of inputparameters and output parameters with less computational complexity.Input parameters may be process step characteristics, output parametersmay be resulting product performance characteristics (includingresulting process characteristics).

The invention can be particularly useful in the production ramp-up, whennew processes and/or new products are introduced. Using simulation atthis stage is advantageous because conventional methods for improvementrely only on measured data, which is not available in quantity duringthe production ramp. The use of simulation results to complementmeasured data is therefore especially valuable.

Also, the invention can be used for volume manufacturing. By applyingthe method according to the present invention to volume manufacturingthe yield is enhanced for a great number of products. The benefit of theinvention is therefore increased.

Further, the TCAD models can be applied to semiconductor manufacturingfor the purpose of predicting parametric chip yields both during andsubsequent to the wafer fabrication manufacturing process, i.e., tomanipulate the manufacturing of either the same product batch or futureproduct batches.

As well, the application of TCAD simulation, TCAD Process Model and/orTCAD Process Compact Model to semiconductor manufacturing processes atmultiple semiconductor manufacturing facilities for the purpose ofunderstanding the electrical differences between the similar but notidentical processes is possible. In this case, measured data from eachindividual manufacturing process can be compared to a common TCADsimulation, TCAD Process Model and/or TCAD Process Compact Model inaddition to being compared to each other. In this case they serve as areference simulation which makes the process differences moreunderstandable.

TCAD Process Compact Models can be obtained from TCAD simulations byperforming a comprehensive Design of Experiment, consisting ofindividual experiments. In the Design of Experiments, input parametersare systematically varied and for each of the variants the outputparameters are determined. As used herein, each combination of inputparameter values and its resulting output parameter values is referredto as a “data set”, and two data sets are considered “different” if thevalue of at least one parameter in one data set differs from the valueof the same parameter in the other data set. The collection of the datasets is sometimes referred to herein as a database, and as used herein,the term “database” does not necessarily imply any unity of structure.For example, two or more separate databases, when considered together,still constitute a “database” as that term is used herein.

The individual experiments are performed using TCAD simulations. Fromthe set of performed experiments a simplified representation is derived,which can approximate for a given set of input parameters the outputparameters. The simplified representation is typically called a responsesurface. The response surface can be represented by functions that arefitted to the performed TCAD experiments by regression, or by tabulationand interpolation of the experiments. Methodologies include responsesurfaces models that consist of polynomial expressions, which areobtained by least squares fitting; neural networks, in which a certaintype of nonlinear function is fitted to data using optimizationalgorithms; or other mathematical functions or tables which can befurther interpolated. The user can choose whether to use methodologiesthat have zero error for the experiments used for fitting, or whether aresidual error in the experimental points is permitted.

FIG. 1 is a flowchart of steps that can be used in creating a TCADProcess Compact Model. In step 110, a TCAD simulation flow is createdthat replicates in simulation the product manufacturing process as wellas the process of determining the product performance. In step 112, theTCAD simulation flow is parameterized with parameters of interest p_(i)(i=1, . . . n) that allow to influence the manufacturing process, andextract the product performance characteristics as values r₁ (i=1, . . ., m).

In step 114, a “Design of Experiment” table is set up with parametricvariations for the parameters p_(i). Typically a systematic design ischosen such as a full factorial 3-level design. Alternatively, otherdesigns can be chosen, such as face-centeredcomposite design, Taguchidesign, or similar. As an example, for a full factorial with 2parameters p_(i) and p₂ with three levels each, for the parameter p₁ thevalues 1, 2 and 3, and for the parameter the values 4, 5, 6, we obtainthe following experimental matrix

Experiment Value p₁ Value p₂ 1 1 4 2 1 5 3 1 6 4 2 4 5 2 5 6 2 6 7 3 4 83 5 9 3 6

In step 116, the TCAD simulations are performed for each of theparameters, obtaining the product performance characteristics r_(i).Each of the experiments will usually take a considerable amount of time,as previously mentioned. Each of the experiments is an evaluation of theTCAD Process Model. As an example, for the above experimental design theresulting table is for 3 performance characteristics r₁, r₂, and r₃

Experiment Value p₁ Value p₂ Value r₁ Value r₂ Value r₃ 1 1 4 5 1766.71828 2 1 5 6 20 127.7183 3 1 6 7 23 218.7183 4 2 4 6 25 71.38906 5 25 7 29 132.3891 6 2 6 8 33 223.3891 7 3 4 7 35 84.08554 8 3 5 8 40145.0855 9 3 6 9 45 236.0855

In step 118, the TCAD Process Compact Model is derived by fittingfunctions r_(j)(p₁, . . . , p_(n)) (j=1, . . . m) to the values in thetable. This can be for example a polynomial functionr_(i)=A*p₁̂2+B*p₂̂2+C*p₁*p₂+D*p₁+E*p₂+F, with coefficients A,B,C,D,E,Fto be determined by a least squares fitting process, resulting, forexample, in the function r₂(p₁, p₂, p₃)=p₁+2*p₂+p₁*p₁+3+p₁*p₂. It shouldbe noted that arbitrary other functions or representations can be used,among others nonlinear functions, fitted by a nonlinear optimizationprocess, and tabulated functions with interpolation. In practice thechoice of approximation function influences accuracy and performance ofthe approximation.

In step 120, The TCAD Process Compact Model is then checked foraccuracy, by comparing values predicted by the TCAD Process CompactModel to values generated by the TCAD Process Model, either in theoriginal experiments used for creating the TCAD Process Compact Model orin an additional set of test experiments. Although there is generally acertain loss of accuracy incurred by using an approximation function, anappropriate choice of “Design of Experiment” and an appropriate choiceof approximation function can bring down the error to a reasonably smallamount. It may be necessary to extend the Design of Experiment and/orchange the approximation function to increase accuracy. This isperformed by going back to step 114 or step 118.

Once the TCAD Process Compact Model is ready for use, the user has asimplified representation of the process-product relationships with aconsiderably lower size and implementational complexity. We thereforerefer to it as a TCAD Process Compact Model, as opposed to a TCADProcess Model.

Example: As an illustration, we show a TCAD Process Model or TCADProcess Compact Model can contain a number of reasonable, but in no waycomplete process parameters and device characteristics, given for theexample of a NMOS and a PMOS device in a CMOS process. Processparameters: p₁) gate oxide thickness, p₂) gate length, p₃) halo implanttilt, p₄) halo implant dose, p₅) final RTP (rapid thermal annealing)temperature. The product performance characteristics are in this caser₁) the threshold voltage V_(T), r₂) the drive current I_(ON), r₃) theleakage current I_(OFF), r₄) the switching speed of an inverter. A3-level, full factorial DoE will result 243 experiments. Typically, gateoxide thickness as well as gate length or critical dimension aremeasured in the process, whereas halo implant tilt and dose as well asfinal RTP temperature are assumed to be given by process equipmentsettings and are not measured. Device performance characteristics V_(T),I_(ON), I_(OFF) are typically measured on the wafer, whereas inverterswitching speed is not typically measured here and serves as a simpleexample for product performance. We will refer to this particular PCMbelow.

The use of TCAD Process Compact Models over the TCAD Process Model canhave considerable advantages over the direct use of TCAD, even thoughthere is an approximation error and therefore a loss of accuracyinvolved. TCAD tools are typically very complex, with the complexityrising with the growing accuracy of physical modeling. In practice, TCADsimulations take considerable time to perform. In addition, TCAD toolsdue to their complexity are in general sufficiently robust for aresearch and development environment, but not for a productionenvironment. TCAD Process Compact Models, on the other side, have muchless complexity, are therefore much more robust towards programfailures, and can be evaluated in a much shorter time. As an example, atypical 2-dimensional process and device simulation using TCAD currentlytakes typically one hour, whereas a TCAD Process Compact Model cantypically be evaluated in less than a microsecond. The disadvantage ofthe process compact model is that only the predefined output parametersare available, whereas each TCAD simulation typically generates acomprehensive representation of a semiconductor device that cansubsequently be examined and probed in arbitrary ways. In the presenttargeted application, the latter is typically not required, and the TCADProcess Compact Model has therefore considerable practical advantages.

As no manufacturing process is perfect, the products obtained in realmanufacturing are subject to manufacturing defects or their performancediffers from the targeted results, resulting in non-optimum defect yieldand non-optimum systematic yield. In semiconductor manufacturing, thisproblem of process defects and process variability is generallyincreasing with smaller technologies. Currently, it is addressed throughyield management systems and simple models to correct the processes.

Some drawbacks of existing techniques are

-   -   that the known models for controlling and correcting processes        at this point are very limited in their applicability, by not        being able to encompass a sufficiently high number of process        parameters and their interactions, or by not being able to        accommodate the nonlinearity of some parameters.    -   that creating process models or process compact models from        purely experimental data requires costly experiments.    -   that not all process parameters can be measured at all or at        reasonable cost and are therefore not amenable to conventional        process control and yield management    -   that not all product performance parameters can be measured at        all or measured at reasonable cost, and are therefore not        amenable to conventional process control and yield management.        It is an object of the present invention to overcome the        drawbacks of the prior art, in particular to provide economical        methods for enhancing the systematic yield in semiconductor        manufacturing, in particular the part of the systematic yield        that is caused by nonoptimality, variation and drift in        semiconductor manufacturing process steps. This object is        addressed with a method, a system and a computer program product        as described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described with respect to particular embodimentsthereof, and reference will be made to the drawings, in which:

FIG. 1 is a flowchart of steps that can be used in creating a TCADProcess Compact Model in accordance with aspects of the invention.

FIGS. 2-4 are flowcharts illustrating different use cases for aspects ofthe invention.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the invention, and is provided in the context ofa particular application and its requirements. Various modifications tothe disclosed embodiments will be readily apparent to those skilled inthe art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present invention. Thus, the present invention is notintended to be limited to the embodiments shown, but is to be accordedthe widest scope consistent with the principles and features disclosedherein.

In FIG. 1, a PCM 122 is shown together with its process parameter inputsand its product performance parameter outputs. The PCM can be used in avariety of different ways to enhance the manufacturing process. Invarious different use scenarios, actual or desired values are providedfor some of the parameters, and actual or desired values are determinedusing the PCM for one or more of the remaining parameters. The“provided” parameters are not limited to the process input parameters,nor are the “determined” parameters limited to the product performanceparameters. In many of the most useful scenarios, in fact, the“provided” parameters include one or more of the product performanceparameters, and the PCM is used to determine one or more of the processinput parameters which either were not measured, or are not susceptibleto measurement. In these cases, optimization algorithms or data miningtechniques can be used with the PCM to determine actual or desiredvalues for the unknown parameters. The use of a PCM instead of a fullTCAD process model to execute these algorithms and techniques rendersthem practical.

Three example use cases will now be described in order to illustratevarious ways in which a PCM can be used to enhance a manufacturingprocess.

Use Case No. 1: Generation of Data for Performance Prediction or YieldManagement, Both Simulated Data Equivalent to Measured Device/productPerformance Data, and Data Predicting Device/product Performance DataThat Is Not Measured.

TCAD allows the user to complement and compare measured data from theproduction process with the corresponding simulated data. FIG. 2 is aflowchart illustrating how a PCM can be used with measured process datato predict a device characteristic by simulation.

In step 210, actual process input parameter values are obtained andaccumulated. Step 210 results in values p₁, . . . , p_(n) characterizingcharacteristical features of the process steps, such as film thickness,gate critical dimension, implant angle and dose, anneal temperature,etc. As used herein, an “actual” value for a parameter is one thatexists and is either measured, or is “assumed” based on equipmentsettings. An “vestimated” value is also one that exists, but it isestimated through the use of TCAD or a TCAD-derived model. The terms“actual” and “estimated” distinguish over values which do not yet exist,such as “desired” and “not-yet-determined” values. A“not-yet-determined” value is one that is not yet in existence,typically because the process steps it would characterize have not yetbeen performed or the partially or fully completed product it wouldcharacterize does not yet exist. If not-yet-determined values arerequired, they can be “predicted” by TCAD or a TCAD-derived model, basedon provided, actual, assumed or estimated, process input parametervalues. For certain purposes not-yet-determined values can also be“assumed” because there is little doubt about what the value will be.One example of an assumed not-yet-determined value is the value of aprocess parameter used in a process step that has not yet occurred, butfor which the equipment that will be used typically produces only onevalue. Another example is a product performance characteristic“predicted” by TCAD or a TCAD-derived model. The terms “actual” and“estimated” also distinguish over “example” values, such as might beused when exploring example process input parameters in an effort toidentify desired values for them which are predicted to yield desiredproduct performance parameters, or such as might be used when creating adata set.

In step 212, the PCM is evaluated for each of the actual process inputparameter values, resulting in predicted values r₁, . . . r_(m). Thepredicted values r_(i), . . . , r_(m) are accumulated and made availablein a data-mining system such as a yield management system for subsequentcomparison or correlation with actual data obtained from a productionlot. In particular, in step 214, actual and/or estimated values areobtained from an actual production lot or lots, and in step 216, thedifference between predicted data and actual or estimated data can beused in data mining techniques used in production for finding processdeviations, process drift, or changing influences by process parametersthat are not captured in the TCAD Process Model or TCAD Process CompactModel.

Example: Referring to the TCAD Process Compact Model described above,gate oxide thickness and gate critical dimension are typically measuredfor each product wafer. For halo implant tilt, halo implant dose and thefinal RTP temperature, the equipment setting values in the correspondingprocess steps can be used. The device performance data V_(T), I_(ON),and I_(OFF) can all be predicted by the TCAD Process Compact Model andbe measured in-line. The comparison of the predicted device performanceand measured device performance, as an example, can be used to analyzewhether any variations in product performance are likely to be caused byrelated variations in gate oxide thickness and gate critical dimension,or whether variations or drifts in halo implant dose, halo implant tilt,or final RTP temperature or a variation of other process parameters arelikely causes for the inconsistency between predicted and observeddevice performance. Furthermore, using the actual inprocess data, it ispossible to predict and therefore monitor non-measured productperformance such as inverter switching speed. The quality of thepredicted product performance data is likely to move synchronously withthe quality measure obtained by comparing the predicted deviceperformance data with the measured device performance data.

A particular advantage of using data from TCAD Process Model and TCADProcess Compact Model is that, by nature, these models are not subjectto noise, uncertainties and drift as occur in the manufacturing process.The combination and correlation of measured data with simulated data istherefore particularly valuable.

In addition to providing simulated product performance data thatreplicate measured product performance data, TCAD Process Models andTCAD Process Compact Models also allow to analyze, regulate and optimizedevice characteristics that are conventionally not measured inmanufacturing because such measurement is fundamentally not possible,difficult or uneconomical. A parameter is considered herein to be“commercially incapable of measurement” if either it cannot be measured,or it can be measured but is deemed not feasible or economical to do soin a commercial context. Examples may be the transient characteristics,noise characteristics, high-frequency characteristics, the performanceof small circuits with particularly high sensitivity to processparameters, or similar. Notably, such additional data for predictedproduct performance can be used in conventional yield managementssystems. On the basis of the predicted product performance the yield ofmanufacturing can be analyzed and improved, with respect to otherperformance criteria than the conventionally measured and availableones.

Use Case No. 2: Inverse Analysis

With the use of the present invention non-measured or non-measurableparameters, e.g., implant doses, implant tilt angles, gate shapecharacteristics, layer thicknesses, or similar, can be calculated withthe simulation software, using other measurements and indirectlyestimating the parameters of interest. This software can be a TCADsimulation, a TCAD Process Model and/or a TCAD Process Compact Model,but is preferably a TCAD Process Compact Model. The non-measurableparameters are reconstructed by inverse analysis of a TCAD Process Modelor TCAD Process Compact Model from measurements of devicecharacteristics of semiconductor devices and/or test structures. Inverseanalysis uses an optimization algorithm in which the input parameterswhose values are known through measurements and output parameters thatare known through measurements are provided (taken as given) and theunknown input parameters to the process compact model, which representthe non-measured parameters, are optimized by changing them in amathematical optimization algorithm until the residual error becomesminimal. FIG. 3 is a flow-chart illustrating major steps in the InverseAnalysis use case.

In step 310, actual values are obtained and accumulated for some of theprocess input parameters and product performance parameters. Thesevalues can be obtained, for example, by measuring on product wafers, orby assumption from the process equipment settings. This results invalues p₁, p₂, . . . , p_(k) characterizing characteristical features ofthe process steps such as film thickness, gate critical dimension,implant angle and dose, anneal temperature, etc. This also results invalues rm₁, rm₂, . . . , rm_(m), characterizing product performance.

In step 312, using a TCAD Process Compact Model with functions r_(i)(p₁,p₂, . . . , p_(k), p_(k)+1, p_(k)+2, . . . , p_(n)) where i=1, . . . ,m, the most likely actual values are determined and accumulated forunknown process input parameters p_(k)+1, p_(k)+2, . . . , p_(n). Thisis performed by selecting weights w_(i) (i=1, . . . , m) for theindividual device performance characteristics, denoting their relativeimportance or quality of measurement, and minimizing the differencelength of the residual error vector w_(i)*(r_(i)−rm_(i)) (i=1, . . . ,m). Typical multidimensional optimization algorithms can be used, suchas the Newton-method, nonlinear simplex method, simulated annealing,genetic algorithms or similar. A beneficial part of the algorithm is thecompensation for any systematic differences between measured valuesr_(i) _(—) _(m) and simulated values r_(i) _(—) _(s) for functionsr_(i)(p₁, . . . , p_(n)). The differences typically arise throughimperfect calibration. While the imperfect calibration has an effect onthe absolute values for r_(i), it has only a small effect on thesensitivity of the function r_(i)(p₁, . . . , p_(n)). It is thereforesufficient to 1) estimate the simulated nominal value r_(i) _(—) _(n) ofthe function r_(i)(p₁, . . . , p_(n)) from evaluating the function fornominal values p₁ _(—) _(n), . . . , p_(n) _(—) _(n), and 2) anddetermining the averages r_(i) _(—) _(m) _(—) _(ave) or medians r_(i)_(—) _(m) _(—) _(median) of a number of measured values from themeasured values r_(i) _(—) _(m). The reconstruction is then made for thecompensated value r_(i) _(—) _(m)−(r_(i) _(—) _(m) _(—) _(median)−r_(i)_(—) _(n)) or r_(i) _(—) _(m)−(r_(i) _(—) _(m) _(—) _(ave)−r_(i) _(—)_(n)), respectively, if subtraction is used for compensation.Alternatively, the compensated value r_(i) _(—) _(m)/r_(i) _(—) _(m)_(—) _(median)*r_(i) _(—) _(n) or r_(i) _(—) _(m)/r_(i) _(—) _(m) _(—)_(ave)*r_(i) _(—) _(n) can be used, respectively, if multiplication isused for compensation.

Example: Using on the TCAD Process Compact Model from the examplesabove, it is possible to attribute any of the differences betweenpredicted and observed product performance data V_(T), I_(ON), I_(OFF)to changes in halo implant dose, halo implant tilt, either each byitself, assuming others with assumed values, in combinations of two, orall together. These process parameters are usually not measured, butassumed. Yet, they may be subject to drift or to variation across awafer. Using the reconstructed (estimated) values for these parameters,it is in turn possible through evaluation of the TCAD Process Model orTCAD Process Compact Model with measured gate oxide thickness, gatecritical dimension, reconstructed halo tilt and dose, and RTPtemperature from the equipment setting, to predict the productperformance characteristic of inverter switching speed.

If the input parameters of the TCAD Process Model or TCAD ProcessCompact Model comprise all relevant sources of variation in the processparameters and the measurements (of process parameters or responses),the resulting estimate can be considered as being correct. If othersources of variation or noise in the manufacturing process and themeasurements exist, the estimation of the unknown parameter is lesscorrect, since the unknown additional variability is attributed to theprocess parameters under study. Since the resulting variability islarger than the real variability, the estimate serves as a useful upperlimit.

Notably, such additional reconstructed data for parameters can be usedin conventional yield managements systems. On the basis of thereconstructed or estimated non-measured or non-measurable parameters theyield of manufacturing can be improved.

Use Case No. 3: Feed-Forward Process Enhancement

According to the third use case, semiconductor manufacturing processesare optimized during one or more manufacturing step by means of TCAD.FIG. 4 is a flowchart illustrating a method for enhancing the yield insemiconductor manufacturing, wherein semiconductor products aremanufactured in a plurality of sequential manufacturing steps.

In step 410, one or more steps of a semiconductor fabrication processare performed, yielding a partially completed or intermediate product.As used herein, the term “process step” can include other process“sub-steps”, which are themselves considered herein to be steps in theirown right.

In step 412, actual values are obtained for process input parametersalready established in the intermediate product. The values can beobtained by measurement or by assumption based on process equipmentsettings. Alternatively, estimated values can be obtained for some orall of the process input parameters, for example using inverse analysisas described above. This results in values p₁, p₂, . . . , p_(k)characterizing characteristic features of the process steps such as filmthickness, gate critical dimension, implant angle and dose, annealtemperature, etc.

In step 414, targeted (desired) product performance is characterizedwith desired values rt₁,rt₂, . . . , rt_(m).

In step 416, using a TCAD Process Compact Model with functions r_(i)(p₁,p₂, . . . , p_(k), p_(k)+1, p_(k)+2, . . . , p_(n)) where i=1, . . . ,m, and values p₁, . . . , p_(k) for the known process input parameters,desired characteristic values are determined for yet to be performedprocess steps p_(k)+1, p_(k)+2, . . . , p_(n). This is performed byselecting weights w_(i) (i=1, . . . , m) for the individual deviceperformance characteristics, denoting their relative importance orquality of measurement, and minimizing the difference length of theresidual error vector w_(i)*(r_(i)−rt_(i)) (i=1, . . . , m). Typicalmultidimensional optimization algorithms can be used, such as theNewton-method, nonlinear simplex method, simulated annealing, geneticalgorithms or similar. A beneficial part of the algorithm is thecompensation for any systematic differences between measured valuesr_(i) _(—) _(m) and simulated values r_(i) _(—) _(s) for functionsr_(i)(p₁, . . . , p_(n)). The differences typically arise throughimperfect calibration. While the imperfect calibration has an effect onthe absolute values for r_(i), it has only a small effect on thesensitivity of the function r_(i)(p₁, . . . , p_(n)). It is thereforesufficient to 1) estimate the simulated nominal value r_(i) _(—) _(n) ofthe function r_(i)(p₁, . . . , p_(n)) from evaluating the function fornominal values p₁ _(—) _(n), . . . , p_(n) _(—) _(n), and 2) anddetermining the averages r_(i) _(—) _(m) _(—) _(ave) or medians r_(i)_(—) _(m) median of a number of measured values from the measured valuesr_(i) _(—) _(m). The determination of optimum parameters is then madefor the compensated target value r_(i) _(—) _(m)−(r_(i) _(—) _(m) _(—)_(median)−r_(i) _(—) _(n)) or r_(i) _(—) _(m)−(r_(i) _(—) _(m) _(—)_(ave)−r_(i) _(—) _(n)), respectively, if subtraction is used forcompensation. Alternatively, the compensated target value r_(i) _(—)_(m)/r_(i) _(—) _(m) _(—) _(median)*r_(i) _(—) _(n) or r_(i) _(—)_(m)/r_(i) _(—) _(m) _(—) _(ave*r) _(i) _(—) _(n) can be used,respectively, if multiplication is used for compensation.

In step 418, subsequent manufacturing steps are performed with p_(k+1),. . . , p_(n) that result from the optimization algorithm, therebyforming a final product or another intermediate product.

Depending on the product it is possible that the steps 412-418 are to berepeated several times until the semiconductor product is finished. Theintermediate product is a partially finished product.

The method can be performed in semiconductor manufacturing on a lotlevel, on a wafer level or on a die level, sometimes also called reticlelevel. On every level, enhancement of the manufacturing yield ispossible, i.e., on a lot level if wafers are manufactured by a lot, on awafer level if wafers are manufactured in single-wafer processing, and,in both cases, on a die level during die-level processing steps.Examples for die-level processing steps are the exposure of a individualdie on the wafer in the stepper, or the annealing of an individual dieon the wafer in a laser annealing step.

Example: Using the TCAD Process Compact Model described above, it ispossible to partially process a wafer, measure gate oxide thickness andgate critical dimension in one or several locations of the productwafer, determine V_(T), I_(ON), I_(OFF) as well as the inverterswitching speed that are most desirable, assume that the final RTPtemperature will not be changed, and, using the described analysisalgorithm, determine the most desirable setting for dose and tilt angleof the halo implant step. If measurements are made in more than onelocation on the wafer, we can determine the best setting from theaverage of the measurements or from an average that is weighted by thearea corresponding to a particular measurement.

As can be seen, a TCAD PCM can be used in a variety of scenarios for theimprovement of manufacturing processes. Many of the scenarios can besummarized using the PCM 122 in FIG. 1, as shown together with itsprocess parameter inputs and its product performance parameter outputs.In some scenarios there are values provided for one or more of theprocess input parameters, whereas in other scenarios there are valuesprovided for one or more of the product performance parameters. In stillother scenarios there are values provided for both process inputparameters and product performance parameters. The provided values maybe in various ones of the scenarios either actual values (measured orassumed based on other knowledge) of process input parameters or productperformance parameters, estimated values of process input parameters orproduct performance parameters based on indirect analysis methods,not-yet-determined values of process input parameters which have not yetoccurred, or desired values of product performance parameters. Inaddition, in some scenarios the PCM 122 is used in the determination ofone or more desired, estimated or predicted values for process inputparameters, whereas in other scenarios the PCM 122 is used in thedetermination of desired, estimated or predicted values for productperformance parameters.

Using this conceptual framework, the following table provides a summarycomparison of certain specific example use cases.

USE PCM TO USE CASE BASED ON DETERMINE EXAMPLE THESE VALUES THESE VALUESPerformance Actual, estimated Predicted values for Prediction for and/ornot-yet- product performance partially completed determined butparameters products assumed values for process input parametersPerformance Actual or estimated Estimated values for Prediction forvalues for process product performance completed products inputparameters parameters Performance Not-yet-determined Predicted valuesfor Prediction for but assumed values product performance hypotheticalfor process input parameters products parameters Inverse Analysis Actualor estimated Estimated values for values for Some Others of the processProcess Input input parameters Parameters and actual values for productperformance parameters Feed Forward for Actual, estimated or Desiredvalues for partially completed not-yet-determined others of the processproducts but assumed values input parameters for some process inputparameters, and desired values for product performance parameters

The method according to the present invention is preferably related tothe manufacturing of semiconductor devices, semiconductor teststructures, and circuits, especially yield-critical circuits. It isapplicable to all semiconductor devices that can be simulated by TCAD.In particular, the manufacturing of transistors can be improvedsignificantly.

Through the use of TCAD models the electrical performancecharacteristics of the semiconductor devices, a semiconductor teststructure or circuit, especially a yield-critical circuit, is optimized.For instance the threshold voltage, drive current or leakage current ofa transistor can be regulated and optimized for a particularapplication. The TCAD model helps to find optimized manufacturingparameters on the basis of parameters measured during the manufacturingprocess.

TCAD Process Models and TCAD Process Compact Models also allow tooptimize for device characteristics that are conventionally not measuredin manufacturing because such measurement is fundamentally not possible,difficult or too uneconomical. Examples are the transientcharacteristics, noise characteristics, high-frequency characteristics,the performance of small circuits with particularly high sensitivity toprocess parameters, or similar. Notably, such additional data forpredicted product performance can be used in conventional yieldmanagements systems. On the basis of the predicted product performancethe yield of manufacturing can be improved.

The improvements over conventional process control methods include theuse of TCAD for process modeling, the TCAD Process Model being morecomprehensive and accurate than other, simplified models, and in the useof TCAD Process Compact Models, which are sufficiently fast, robustaccurate and embeddable (meaning that they can easily be integrated intoother manufacturing software environments for process control and yieldmanagement) to allow deployment and use in a manufacturing environment.

As used herein, a given event or information item is “responsive” to apredecessor event or information item if the predecessor event orinformation item influenced the given event or information item. Ifthere is an intervening processing step or time period, the given eventor information item can still be “responsive” to the predecessor eventor information item. If the intervening processing step combines morethan one event or information item, the result of the step is considered“responsive” to each of the event or information item. If the givenevent or information item is the same as the predecessor event orinformation item, this is merely a degenerate case in which the givenevent or information item is still considered to be “responsive” to thepredecessor event or information item. “Dependency” of a given event orinformation item upon another event or information item is definedsimilarly.

The foregoing description of preferred embodiments of the presentinvention has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Obviously, many modificationsand variations will be apparent to practitioners skilled in this art. Inparticular, and without limitation, any and all variations described,suggested or incorporated by reference in the Background section of thispatent application are specifically incorporated by reference into thedescription herein of embodiments of the invention. The embodimentsdescribed herein were chosen and described in order to best explain theprinciples of the invention and its practical application, therebyenabling others skilled in the art to understand the invention forvarious embodiments and with various modifications as are suited to theparticular use contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalents.

1. A manufacturing process method, comprising the steps of: developing asimulation model of a manufacturing process, the simulation modelpredicting, in dependence upon a plurality of process input parameters,a value for a performance parameter of a product to be manufacturedusing the manufacturing process; developing a process compact model ofthe manufacturing process in dependence upon the simulation model, theprocess compact model calculating a value for the performance parameterin dependence upon the plurality of process input parameters; andestimating a value for the product performance parameter in dependenceupon the simulation model, and further in dependence upon at least onemember of the group consisting of an actual values for a provided subsetof the process input parameters and estimated values for a providedsubset of the process input parameters, wherein the step of estimatingcomprises the step of estimating a value for the product performanceparameter in dependence upon the process compact model.
 2. A methodaccording to claim 1, further comprising the step of manufacturing theproduct, prior to the step of estimating.
 3. A method according to claim1, wherein the step of estimating comprises the step of estimating avalue for the product performance parameter in dependence upon thesimulation model and further in dependence upon actual values for theprovided subset of the process input parameters, further comprising thestep of measuring the values in the provided subset of the process inputparameters.
 4. A method according to claim 1, wherein the step ofestimating comprises the step of estimating a value for the productperformance parameter in dependence upon the simulation model andfurther in dependence upon estimated values for the provided subset ofthe process input parameters, further comprising the step of estimatingthe values in the provided subset of process input parameters independence upon the simulation model, and further in dependence upon atleast one member of the group consisting of actual values for a subsetof the process input parameters, estimated values for a subset of theprocess input parameters, and an actual value for a different productperformance parameter.
 5. A method according to claim 1, wherein theproduct performance parameter is commercially incapable of measurement.6. A manufacturing process method, comprising the steps of: developing asimulation model of a manufacturing process, the simulation modelpredicting, in dependence upon a plurality of process input parameters,a value for a performance parameter of a product to be manufacturedusing the manufacturing process; performing a first manufacturingprocess step in the manufacture of the product, the first manufacturingprocess step resulting in an intermediate product; and subsequentlypredicting a value for the product performance parameter in dependenceupon the simulation model, and further in dependence upon an assumedvalue for a particular one of the process input parameters to be used ina second manufacturing process step after the first manufacturingprocess step.
 7. A method according to claim 6, wherein the step ofpredicting a value for the product performance parameter is performedfurther in dependence upon an actual value for one of the process inputparameters used in developing the intermediate product.
 8. A methodaccording to claim 6, wherein the step of predicting a value for theproduct performance parameter is performed further in dependence upon anestimated value for one of the process input parameters used indeveloping the intermediate product.
 9. A method according to claim 6,further comprising the step of developing a process compact model of themanufacturing process in dependence upon the simulation model, theprocess compact model calculating a value for the performance parameterin dependence upon the plurality of process input parameters, andwherein the step of predicting comprises the step of predicting a valuefor the product performance parameter in dependence upon the processcompact model.
 10. A method according to claim 6, further comprising thestep of performing a second process step in the manufacture of theproduct, after the step of predicting.
 11. A method according to claim10, further comprising the steps of: comparing the value obtained in thestep of predicting a value for the product performance parameter, with adesired value for the product performance parameter; and in response tothe step of comparing, adjusting at least one of the process inputparameters for the step of performing a second process step.
 12. Amethod according to claim 10, wherein the step of performing a secondprocess step comprises the steps of: determining a desired value for aparticular one of the process input parameters used in the secondprocess step, in dependence upon the simulation model and further independence upon a desired value for the product performance parameter;and performing the second process step in dependence upon the desiredvalue for the particular process input parameter.
 13. A manufacturingprocess method, comprising the steps of: developing a simulation modelof a manufacturing process, the simulation model predicting, independence upon a plurality of process input parameters, a value for aperformance parameter of a product to be manufactured using themanufacturing process; performing a first process step in themanufacture of the product, the first process step resulting in anintermediate product; subsequently determining a desired value for aparticular one of the process input parameters to be used in a secondprocess step in the manufacture of the product, in dependence upon thesimulation model, and further in dependence upon a desired value for theproduct performance parameter, and further in dependence upon a memberof the group consisting of an estimated value for a process inputparameter used in the development of the intermediate product and anassumed value for a process input parameter to be used in a process stepafter the first process step; and performing the second process step onthe intermediate product in dependence upon the desired value for theparticular process input parameter.
 14. A method according to claim 13,further comprising the step of developing a process compact model of themanufacturing process in dependence upon the simulation model, theprocess compact model calculating a value for the performance parameterin dependence upon the plurality of process input parameters, andwherein the step of determining comprises the step of determining thedesired value for the particular process input parameter in dependenceupon the process compact model.
 15. A method according to claim 14,wherein the step of developing a process compact model comprises thesteps of: executing the simulation model to develop a plurality ofdifferent simulation data sets, each given one of the simulation datasets including a value for each of the process input parameters and alsoincluding the value for the performance parameter predicted by thesimulation model from the process input parameter values in the givendata set; and deriving the process compact model from the simulationdata sets.
 16. A method according to claim 13, wherein the step ofdetermining comprises the step of using an optimization algorithm todetermine the desired value for the particular process input parameter,in dependence upon the simulation model, the desired value for theproduct performance parameter, and the member of the group consisting ofan estimated value for a process input parameter and an assumed valuefor a process input parameter.
 17. A method according to claim 13,wherein the step of determining comprises the step of determining thedesired value for the particular process input parameter in dependenceupon an estimated value for a process input parameter used in thedevelopment of the intermediate product, further comprising the step ofdetermining the estimated value in dependence upon the simulation model,and further in dependence upon at least one member of the groupconsisting of an actual value for a different one of the process inputparameters used in the development of the intermediate product, and anestimated value for one of the process input parameters used in thedevelopment of the intermediate product.
 18. A manufacturing processsystem, comprising: a process compact model of a manufacturing process,the process compact model predicting a value for a performance parameterin dependence upon a plurality of process input parameters; andestimating means for estimating a value for the product performanceparameter in dependence upon the process compact model, and further independence upon at least one member of the group consisting of actualvalues for a provided subset of the process input parameters andestimated values for a provided subset of the process input parameters.19. A manufacturing process system, comprising: a simulation model of amanufacturing process, the simulation model predicting, in dependenceupon a plurality of process input parameters, a value for a performanceparameter of a product to be manufactured using the manufacturingprocess; predicting means for, after performance of a firstmanufacturing process step in the manufacture of the product, the firstmanufacturing process step resulting in an intermediate product,predicting a value for the product performance parameter in dependenceupon the simulation model, and further in dependence upon an assumedvalue for a particular one of the process input parameters to be used ina second manufacturing process step after the first manufacturingprocess step.
 20. A manufacturing process system, comprising: asimulation model of a manufacturing process, the simulation modelpredicting, in dependence upon a plurality of process input parameters,a value for a performance parameter of a product to be manufacturedusing the manufacturing process; and determining means for, afterperformance of a first process step in the manufacture of the product,the first process step resulting in an intermediate product, determininga desired value for a particular one of the process input parameters tobe used in a second process step in the manufacture of the product, thedetermining means determining the desired value in dependence upon thesimulation model, and further in dependence upon a desired value for theproduct performance parameter, and further in dependence upon a memberof the group consisting of an estimated value for a process inputparameter used in the development of the intermediate product and anassumed value for a process input parameter to be used in a process stepafter the first process step.
 21. A manufacturing process system,comprising: a process compact model of a manufacturing process, theprocess compact model predicting, in dependence upon a plurality ofprocess input parameters, a value for a performance parameter of aproduct to be manufactured using the manufacturing process; anddetermining means for, after performance of a first process step in themanufacture of the product, the first process step resulting in anintermediate product, determining a desired value for a particular oneof the process input parameters to be used in a second process step inthe manufacture of the product, the determining means determining thedesired value in dependence upon the process compact model, and furtherin dependence upon a desired value for the product performanceparameter, and further in dependence upon a member of the groupconsisting of an estimated value for a process input parameter used inthe development of the intermediate product and an assumed value for aprocess input parameter to be used in a process step after the firstprocess step.